The human population on Earth is expanding rap-idly (Steck et al. 2010), and artificial light is funda-mental to the functioning of modern society. Yetwhile humans have become accustomed to artificialcycles of light and dark, other species, especiallynocturnal or crepuscular organisms, may dependupon natural light cycles for successful functioning(Kramer & Birney 2001). As a result, the amount ofartificial light now used around the world is causing
some concern among scientists and conservationists(Rich & Longcore 2006). On a global scale, the growthof human populations in coastal zones is occurringfaster than human population growth in general(Nicholls 1995). Due to this disproportionate growth,coastal habitats have become some of those most vul-nerable to light pollution (Bird et al. 2004).
Marine turtles are arguably the best-known exam-ple of an organism adversely affected by coastallighting (Witherington & Martin 2000, Salmon 2003).Dependence upon visual brightness cues for ‘sea-
Coastal light pollution and marine turtles: assessing the magnitude of the problem
Ruth L. Kamrowski1,*, Col Limpus2, James Moloney1, Mark Hamann1
1School of Earth and Environmental Sciences, James Cook University, Townsville, Queensland 4811, Australia2Department of Environment and Heritage Protection, PO Box 2454, Brisbane, Queensland 4001, Australia
ABSTRACT: Globally significant numbers of marine turtles nest on Australian beaches; however,the human population of Australia is also heavily concentrated around coastal areas. Coastaldevelopment brings with it increases in artificial light. Since turtles are vulnerable to disorienta-tion from artificial light adjacent to nesting areas, the mitigation of disruption caused by light pol-lution has become an important component of marine turtle conservation strategies in Australia.However, marine turtles are faced with a multitude of anthropogenic threats and managers needto prioritise impacts to ensure limited conservation resources can result in adequate protection ofturtles. Knowledge of the extent to which nesting areas may be vulnerable to light pollution isessential to guide management strategies. We use geographical information system analysis toover-lay turtle nesting data onto night-time lights data produced by the NOAA National Geophys-ical Data Center, to assess the proportion of marine turtles in Australia potentially at risk from lightpollution. We also identify the Australian nesting sites which may face the greatest threat fromartificial light. Our assessment indicates that the majority of nesting turtles appear to be at lowrisk, but population management units in Western Australia and Queensland are vulnerable tolight pollution. The risk to turtles from light generated by industrial developments appears sig -nificantly higher than at any other location. Consequently, managers of turtle management unitsin regions of proposed or on-going industrial development should anticipate potentially disruptedturtle behaviour due to light pollution. Our methodology will be useful to managers of turtles elsewhere.
Resale or republication not permitted without written consent of the publisher
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Endang Species Res 19: 85–98, 2012
finding’, means the orientation of hatchling marineturtles is disrupted by artificial lighting close to thenesting beach (Witherington & Martin 2000, Tuxbury& Salmon 2005). This can have serious negative con-sequences for hatchling survival. Protracted periodsspent crawling on the beach increase predation risk,as well as wasting the limited energy stores hatch-lings possess from their yolk, which are necessary forcrucial offshore migration (Salmon 2006, Hamann etal. 2007, Booth & Evans 2011).
Coastal lighting has also been reported to discour-age adult females from nesting on particular stretchesof beach (Salmon et al. 2000). Many marine turtlenesting beaches are located adjacent to human pop-ulations, or to areas earmarked for development. Ashuman population centres expand and light levels incoastal regions around the world increase, the avail-ability of naturally dark beaches attractive to nestingfemales is likely to decrease. This may lead to higherconcentrations of nests on beaches deemed darkenough for nesting purposes (Salmon 2006). How-ever, beaches with higher density nesting face agreater likelihood of nest destruction by other nest-ing females (Bustard & Tognetti 1969) and potentiallyincreased hatchling predation (Pilcher et al. 2000,Wyneken et al. 2000). In addition, shifts in nestingdistribution may take hatchlings away from theoceanographic features which are most favourablefor dispersal (Putman et al. 2010, Hamann et al.2011).
Most studies concentrating on disruption to mar-ine turtles as a result of artificial lights have beenbeach specific or limited to one region (e.g. Wither-ington 1991, Peters & Verhoeven 1994, Salmon etal. 2000, Salmon 2003, Bertolotti & Salmon 2005,Pendoley 2005, Stapput & Wiltschko 2005, Hare-wood & Horrocks 2008). However, the extent ofartificial light usage is visible from space. Globalmeasurements of artificial light have been collectedas part of the US Air Force Defense MeteorologicalSatellite Program (DMSP) Operational LinescanSystem (OLS) since 1992 (Elvidge et al. 2007).These data are freely available from the NOAA’sNational Geophysical Data Center (NGDC), andconsist of cloud-free composites created from multi-ple nightly orbits by the DMSP satellites each year(Elvidge et al. 1997, 2001). The DMSP images havebeen em ployed for a diverse range of studies inrecent years (e.g. Aubrecht et al. 2008, Nagatani2010, Badarinath et al. 2011), yet few studies haveutilised these global datasets with reference tonesting turtles (but see Ziskin et al. 2008 andSalmon et al. 2000).
The wavelengths recorded by the OLS sensor areconsistent with wavelengths disruptive to adult andhatchling marine turtles. Both adult and hatchlingturtles have been shown to be responsive to wave-lengths within the 440 to 700 nm range, with greatestsensitivity at longer wavelengths (approximately580 nm) for adults (Levenson et al. 2004) and from350 to 540 nm for hatchlings (Witherington & Bjorn-dal 1991, Witherington & Martin 2000, Horch et al.2008). The OLS possesses a broad spectral responsefrom 440 to 940 nm, making these datasets a poten-tially useful tool for the assessment of light pollutionimpacts on turtle nesting sites (Magyar 2008).
The Australian coastline supports large and glob-ally important marine turtle nesting aggregations(Limpus 2009). However, >80% of Australia’s inhab-itants live in coastal areas (Hennessy et al. 2007), andmost of the current population growth, ex cludingcapital cities, is occurring in coastal regions (Luck2007). Currently, most beaches in northern Australiaused by nesting turtles do not experience the samelevels of human encroachment (and the associatedimpacts from light pollution) that have occurred inmany other parts of the world (Chatto & Baker 2008,Limpus 2009). However, coastal development innorthern Australia is increasing. For ex ample, thesouth-east portion of Queensland (QLD) and northWestern Australia (WA), both of which support nest-ing by multiple turtle species (Limpus 2009), are eachexperiencing rapid urban growth and in dustrialdevelopment (SEQ Catchments 2010, AustralianBureau of Statistics 2012).
In Australia, all marine turtles are protected underthe Australian and State Governments’ conservationlegislation (Limpus 2009), and the disruptive influ-ence of light pollution is widely acknowledged (e.g.Department of Environment and Con servation 2007,2008). Management actions considered necessary toaddress this issue include the identification of priorityareas affected by artificial light. Yet, implementingmanagement strategies can be expensive and timeintensive (Fuentes et al. 2009). Knowledge of areas athighest risk from light pollution is important to per-mit management re sources to be allocated mosteffectively (e.g. Fuentes et al. 2011).
We used the 2006 Radiance Calibrated Lightsdataset from the NGDC to address 2 specific aims.Firstly, we assessed the proportion of nesting marineturtles within Australia that are exposed to coastallight pollution as it is detected from space. This pro-portion was assessed at both a national and ‘popula-tion management unit’ scale, since it is importantthat the severity of threats to specific population
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Kamrowski et al.: Light pollution and marine turtles
units is determined so as to allow targeted manage-ment ap proa ches, thereby ensuring that conserva-tion strategies are as effective as possible (Dobbs etal. 1999, Wallace et al. 2010). Secondly, we identi-fied those nesting sites in Australia which may facethe greatest threat from artificial light. This is thefirst study of its kind. The results will be beneficialfor both managers and scientists, since this methodallows the identification of nesting locations vulner-able to coastal light pollution at ecologically rele-vant scales, which can be used in combination withexisting on-the-ground data to inform and guideconservation strategies or environmental impactassessments. The methods utilised in this study willalso prove a useful tool for managers of marine turtles outside of Australia, in any location wherelimited resources require targeted conservationmeasures.
MATERIALS AND METHODS
Study species
Marine turtle nesting beaches occur across theentire northern coast of Australia, from northernNew South Wales to Shark Bay in WA. Six of the 7extant species of marine turtles (loggerhead Carettacaretta, green Chelonia mydas, hawksbill Eretmo -chelys imbricata, olive ridley Le pidochelys olivacea,flatback Natator depressus, leatherback turtles Der-mochelys coriacea) nest in Australia, with only theKemp’s ridley turtle L. kempii absent. Nesting andhatchling emergence occur at different times of theyear, depending on the species and population man-agement unit (Limpus 2009). Due to the minor andsporadic nesting of leatherback turtles in Australiathis species was not included in our analysis.
Data acquisition
Turtle nesting data
We extracted the locations of nesting beaches forall turtle species within Australia from the QDERM(Queensland Department of Environment and Re -source Management) turtle database, September2003. These data consisted of geographical informa-tion system (GIS) point shapefiles, with a geographicposition (latitude/longitude) for each nesting beach,as well as an estimate of the number of femalesbreeding each year at the beach. The use of adult
females, excluding adult males and immature turtles,is a commonly used metric for assessing populationunits of marine turtles (Heppell et al. 2003). The esti-mates used here are the results of numerous studies(see Limpus 2009 for a review), and are the bestknown data available. Gaps in the database werefilled using expert opinion from local government orindustry turtle project staff.
Population unit data
Population genetic structures for green, logger-head, flatback and hawksbill turtles in Australia havebeen extensively investigated (Bowen et al. 1992,Broderick et al. 1994, Dobbs et al. 1999, Limpus et al.2000, Dethmers et al. 2006, Conant et al. 2009, Lim-pus 2009). Only 1 discrete population managementunit of olive ridley turtles is currently recognised inAustralia, although this is likely to evolve as moregenetic research is conducted. There are nu merousterms in current usage within the scientific literatureto describe population units of marine turtles. We fol-low the terminology used by Dethmers et al. (2006),and refer to each population unit as a ‘managementunit’.
Satellite data
We obtained the 2006 DMSP-OLS raster image ofradiance-calibrated night time light data from theNGDC archive (National Geophysical Data Centre2006). These data were collected by Satellite F16 andare the most recent radiance-calibrated night timelight products available. The DMSP satellite flies in asun-synchronous low earth orbit (833 km mean alti-tude), and orbits the planet 14 times each day with abroad field of view (approximately 3000 km swathwidth), allowing complete coverage of the globe tobe obtained in every 24 h period. The OLS sensorcontains a photomultiplier tube (PMT), which inten-sifies the visible band signal at night, and captures30 arc second resolution grids. This grid cell size cor-responds to approximately 1 km2 at the equator(Elvidge et al. 1997, Aubrecht et al. 2010). The night-time pass occurs between 20:30 and 21:30 h eachnight (Elvidge et al. 2001). Turtle nesting and hatch-ling emergence occur throughout the night, withpeak hatchling emergence occurring between 20:00and 24:00 h (Limpus 1971, Gyuris 1993). Thus, thistime period is suitable for assessing the risk to turtlesfrom artificial lights.
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Pre-assessment
Preparation of shapefiles
The night-light data was obtained in a geographiccoordinate system appropriate for global datasets(GCS_WGS_1984). Once the data pertaining to Aus-tralia had been extracted using ESRI ArcGIS 9.3.1(Fig. 1), the data were transformed into the relevantAustralian coordinate system (GCS_GDA_1994),which matched the geographic coordinate system ofthe nesting data. For each management unit, night-light and turtle nesting data were then further ex -tracted and projected into the appropriate coordinatesystem (GDA_1994_MGA_Zone_49 to 56).
Preparation of night-light pixel data
Pixel values within the radiance-calibrated lightsproduct were converted into a measure of radiance(W m−2 sr−1) (sr: steradian) using the conversion fac-tor provided by the NGDC (see www.ngdc.noaa.gov/dmsp/ data/ radcal/ readme.txt). The radiance datawere converted into luminance data (cd m−2) to per-mit a more intuitive measure of night-time light con-centrations, since radiance (a radiometric unit) de -scribes all wavelengths of light emitted by a source,whereas luminance (a photometric unit) is a measureof the electromagnetic radiation detectable by an ob -server (Palmer 1999).
Converting between radiance and luminance ispossible, but observers are not equally sensitive to all
wavelengths (Narisada & Schreuder 2004). All pho-tometry is based on the standard visibility curve (CIE1932) designed for the photopic (light-adapted) visionof humans (Narisada & Schreuder 2004), which peaksat 555 nm. The design of artificial light sources is alsorelated to this curve, since illumination levels gener-ated by most light sources result in light-adaptedvision (Zissis et al. 2007).
Recent research has discovered that the visual sen-sitivity of both adult and hatchling marine turtlesshow similarities to human vision. Both are sensitiveto wavelengths in the visible part of the spectrum,with peak sensitivity found for green wavelengths atapproximately 540 nm in hatchlings (Horch et al.2008) and at approximately 580 nm in adults (Leven-son et al. 2004). At present there is no luminosity func-tion of photopic vision available for turtles; however,given the similarities in visual sensitivity and also thewavelengths recorded by the OLS sensor, for the pur-poses of the present study, it was considered sufficientto convert between the units using values from thespectral luminous efficiency for human photopic vision.
Radiance values were converted into luminancevalues using the following equation, which repre-sents a weighting of the radiance spectral term foreach wavelength in relation to the visual response atthat wavelength (Palmer 1999):
(1)
where Xv is the luminous intensity (cd m−2), Km is theconstant scaling factor (683 for photopic vision;Hentschel 1994), Xλ is the corresponding radiantintensity (W m−2 sr−1, in nm), Vλ is the curve for pho-topic vision and λ is wavelength.
Each pixel could then be classified into a level cor-responding to a ratio between artificial light and nat-ural night-time brightness below the atmosphere(Cinzano et al. 2001a) (Table 1). Natural night-timebrightness varies depending upon numerous factors,including geographical position, solar activity, timefrom sunset and sky area observed (Cinzano et al.2001b). Since these details were not available foreach nest site, we followed the methodology of Cin-zano et al. (2001a) and used an average naturalnight-time brightness below the atmosphere of 2.52 ×10−4 cd m−2 (Garstang 1986). The International Astro-nomical Union (IAU) recommends that night-timebrightness should not be increased by >10% (ap -prox imately 200 × 10−6 cd m−2) as a result of artificiallighting (Smith 1979). Consequently a 10% increasein night-sky brightness above natural levels is gener-ally accepted as implying light pollution; this corre-sponds with Category 2 shown in Table 1.
X K X V dv =∞
∫m λ λ λ0
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Fig. 1. Night-time lights of Australia. Image and data pro-cessing of night-light data by NOAA’s National GeophysicalData Center. Defense Meteorological Satellite Program data
collected by the US Air Force Weather Agency
Kamrowski et al.: Light pollution and marine turtles
How bright a light appears to a turtle depends onseveral spectral characteristics of the light, i.e. lightintensity, wavelength and turtle spectral sensitivity(Pendoley 2005). Marine turtle hatchlings are sensi-tive to very low light intensities across the visiblespectrum (Witherington & Bjorndal 1991), but partic-ularly be tween violet and green wavelengths (400 to500 nm). Since the satellite data we used includewavelengths within this range, we reasonably assumethat light levels categorised as ‘light pollution’ in thepresent study are visible to turtles. Moreover, giventhat very little light is necessary to disrupt the orien-tation of hatchlings (Witherington & Martin 2000), webelieve that the threshold of light pollution utilisedhere is relevant to turtles.
Analysis of light proximity to nesting locations
Nesting beach sites for each species were overlaidonto the night-light images, and a buffer was drawnaround each nesting site. The data collected by theDMSP sensors corresponded to an area greater thanthat of actual light sources on the ground (Rodrigueset al. 2012) due to the phenomenon of ‘skyglow’,which refers to the dome of light projected upwardsand outwards from urban areas at night (Chalkias etal. 2006). Skyglow is considered to contribute signif-icantly to ecological impacts from light pollution(Rich & Longcore 2006, Kyba et al. 2011). For exam-ple, light generated by an aluminium refinery inQLD, Australia, disrupted marine turtle orientation18 km away (Hodge et al. 2007). Consequently, totake potential effects of skyglow from urban areasinto account, but allowing for small location inaccu-racies in overlaying transformed and projected datalayers, we followed the methodology used by Aub -recht et al. (2008) and used a buffer with a radius of25 km.
Given the low spatial resolution of the night-timelight data (Elvidge et al. 1997), as well as other fac-tors which may influence the impact of artificiallights close to nesting beaches, such as barriers,cloud cover and moon phase (Salmon & Witherington1995, Witherington & Martin 2000, Kyba et al. 2011),2 measures were used to estimate the potential riskof light pollution faced by each species of nesting turtle — as a means of avoiding false precision. Thebuffer (25 km radius) surrounding each nest siteencompassed approximately 2400 pixels, each ofwhich possessed a value corresponding to theamount of light emitted in that area. The mean andmaximum pixel values within each buffer were cal-culated using the zonal statistics tool and Hawth’sTools extension (Beyer 2004) in ArcGIS. These valueswere then assigned into one of the light pollution cat-egories (as per Cinzano et al. 2001a) using the valuesgiven in Table 1. This gave 2 potential risk values foreach site: ‘mean light exposure’ calculated from themean pixel value and ‘maximum light exposure’ cal-culated from the maximum pixel value. Using themaximum pixel value provides an indication of thehighest amount of light potentially visible to turtles ateach site, and as such is the high-risk scenario. Themean pixel value was calculated across the entirearea encompassed by each buffer, to effectively‘smooth out’ the amount of artificial light emitted inthat area (since light levels will be highest in areaswhere bright lights are located, decreasing as distance from the light source increases), hence pro-viding a diffuse measure of light pollution within aparticular buffer area. This was used to provide asecondary measure of risk given that nesting turtlesmay not be directly exposed to the highest levels oflight present in the immediate area, but would stilllikely be susceptible to skyglow effects.
Next, to determine the sites potentially at highestrisk from light pollution for each species and man-
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Category Pixel value Radiance value Luminance value Ratio over (risk value) (W m2 sr−1) (cd m−2) natural brightness
Table 1. Quantification of light pollution, using ratios according to Cinzano et al. (2001a). The categories and risk values refer to the present study
Endang Species Res 19: 85–98, 2012
agement unit, we calculated the percentage nestingthat occurred at each nesting location, both nation-ally and within each management unit. Then weweighted each site for potential risk, by multiplyingthe percentage nesting by the mean and maximumlight exposure risk values, to give 2 potential meas-ures of exposure to light pollution (presented asmedian values ± standard deviations).
Data analysis
Data were tested for normality using the Kol-mogorov-Smirnoff test. Since data were not found tobe normally distributed, comparisons of light expo-sure between population management units wereassessed using the Mann-Whitney U-test and theKruskall-Wallis test. Post hoc pairwise comparisonsof the latter were carried out using Dunn-Bonferronitests (Dunn 1964). All data were analysed using IBMSPSS 20 statistical software.
RESULTS
National light pollution exposure
Nesting sites for loggerhead, green, hawksbill andflatback turtles in Australia appear to be exposed tovarying degrees of light pollution (Table 2). How-ever, despite the broad geographic scale of impact,the majority of marine turtle nesting sites in Australiaappear minimally affected by either level of light pollution exposure (Table 2).
Management unit light pollution exposure
The above analysis was repeated with the speciesnesting site data merged into management units(Bowen et al. 1992, Broderick et al. 1994, Dobbs et al.1999, Limpus et al. 2000, Dethmers et al. 2006, Lim-pus 2009, Wallace et al. 2010).
Loggerheads
There are 2 management units of loggerheads inAustralia: the WA management unit, which occursfrom Dirk Hartog Island to the Muiron Island region,and the eastern Australian management unit, whichis concentrated on the mainland coast of southeastQLD, the islands in the southern Great Barrier Reef(GBR) and minor nesting sites in New Caledonia andVanuatu (Limpus 2009).
Using the maximum light exposure values, wefound more than a third of nesting WA log ger headsand 43.9% of the eastern Australian loggerheadswere potentially exposed to light pollution (Table 3).Indeed a maximum light pollution weighting of461.54 occurred for WA loggerheads (307.7 ± 217.6),which is significantly higher than the maximumweighted exposure for eastern Australian logger-heads (max. = 80.6; median = 8.06 ± 31.76; Mann-Whitney U = <1, n1 = 2, n2 = 30, p < 0.05).
However, when using the mean light exposure values, we found that, although the WA loggerheadsappeared relatively unaffected by light pollution,22% of the nesting sites for the eastern Australianmanagement unit had a light pollution exposure
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Ratio over Light Proportion nesting (%)natural pollution Cc Cm Ei Lo Ndbrightness category Max. Mean Max. Mean Max. Mean Max. Mean Max. Mean
Table 2. Proportion of nesting in Australia, by each species, potentially at risk from each category of light pollution, using themean (mean light exposure) and maximum (maximum light exposure) pixel values from the radiance calibrated light datawithin a 25 km radius buffer surrounding each nest site. Cc: loggerhead Caretta caretta; Cm: green Chelonia mydas; Ei:
Kamrowski et al.: Light pollution and marine turtles
weighting of 8.96 (2.7 ± 3.84); thus the sites arepotentially at risk from light pollution (Table 3).
Greens
There are 7 recognised green turtles managementunits in Australia (Dethmers et al. 2006). Only a smallpercentage of nesting sites for 3 of the managementunits were determined to be potentially at risk fromlight pollution (Table 3). The exception to this wasthe North West Shelf management unit in WA, whichshowed a large proportion of nesting sites potentiallyat risk from both levels of light exposure (39% of theNorth West Shelf green turtle nesting areas high-lighted using the mean light exposure values, and68%, using the maximum light exposure values).
There was a statistically significant difference be -tween the maximum light exposure of the 3 greenturtle management units indicated as exposed tolight pollution (Kruskal-Wallis χ2[3, N = 40] = 23.07,p < 0.01). Pair-wise comparisons indicated that riskof light pollution for nesting turtles on the NorthWest Shelf (658.54; 197.6 ± 196.03) was significantlyhigher than for all other green turtle managementunits. Also, in eastern Australia, the risk of lightpollution for green turtles nesting in the southernGBR stock (16.93; 1.69 ± 6.96) was significantlyhigher than for the northern GBR stock (0.33; 0.22± 0.15).
Using the mean light exposure values, green tur-tles nesting in the North West Shelf (24.39; 0 ± 8.1)are exposed to a significantly higher potential riskfrom light pollution compared to green turtles in theGBR (northern GBR: 0.11; 0.07 ± 0.05; southern GBR:1.88; 0.19 ± 0.62) (Kruskal-Wallis χ2[2, N = 13] = 7.67,p < 0.01).
Hawksbills
Three hawksbill turtle management units arerecognised in Australia (Broderick et al. 1994, Dobbset al. 1999, Limpus et al. 2000). Using the maximumlight exposure values, a large proportion of all 3 werepotentially exposed to light pollution (Table 3). Mostnotable was hawksbill nesting in WA, for which99.8% of nesting appeared to be exposed. The maxi-mum light pollution weighting for hawksbills in WA(1225.42; 673.98 ± 636.75) was significantly higherthan for hawksbills in the Gulf of Carpentaria (53.05;17.68 ± 18.12), and for hawksbills in the Torres Straitand northern GBR (84.59; 0.85 ± 21.99) (Kruskal- Wallis χ2[2, N = 46] = 23.88, p < 0.01).
When employing the mean light exposure values, alarge proportion of hawksbill nesting in WA remainedhighlighted as being at potential risk from light pollu-tion, with an exposure weighting of 45.39 (4.54 ±23.58), but the other management units were not de-termined to be at significant potential risk. The small
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Turtle species Population Risk from mean Risk from maximum management unit light exposure light exposure (%, using mean pixel value) (%, using max. pixel value)
Loggerhead Western Australia 0 34.2 Eastern Australia 21.5 43.9
Green North West Shelf 39 68.3 Scott Reef 0 0
Ashmore Reef 0 0 Gulf of Carpentaria 0 4.5 Northern GBR <1 <1 Coral Sea 0 0 Southern GBR 2.2 3.8Hawksbill Western Australia 54.5 99.8 Gulf of Carpentaria 3.5 41.5 Northern GBR & Torres Strait 0 31.4
Olive ridley Northern Australia 0 9.8Flatback North West Shelf 59.06 87.4 Western Northern Territory 0 0
Gulf of Carpentaria & Torres Strait <1 61 Eastern Australia 24.2 50.1
Table 3. Proportion of each population management unit of marine turtles in Australia located in nesting areas potentially ex-posed to artificial lights brighter than the threshold level of light pollution, i.e. light exposure of Category 2 or above (see
Table 1). GBR: Great Barrier Reef
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sample size of affected sites precluded statisticalanalysis, but the medians indicated that the WA man-agement unit remains at higher risk from light pollu-tion than hawksbills nesting in northern Australia.
Olive ridleys
There is currently only 1 recognised managementunit of olive ridley turtles in Australia (Limpus 2009).The nesting sites for this management unit appearedrelatively unaffected by light pollution. The meanlight exposure values indicated that none of the nest-ing sites appeared to be exposed to light pollution,and, using the maximum light exposure values, only4 out of 25 nesting sites (9.8% of nesting olive ridleys)were potentially exposed to light pollution of Cate-gories 6 and 7 (Table 2).
Flatbacks
Four flatback turtle management units are cur-rently recognised in Australia (Limpus 2009), al -though with on-going genetic research this is likelyto evolve over time. Flatback turtles which nest in thewestern Northern Territory appeared largely unex-posed to light pollution (Table 3). However, for theother 3 management units when using the maximumlight exposure values, large nesting proportionsappeared potentially at risk from light pollution,whereas only the North West Shelf and eastern Aus-tralia management units were identified to be atpotential risk when employing the mean light expo-sure values.
The maximum light exposure values gave a maxi-mum weighting of 637.8 for flatback turtles on theNorth West Shelf (330 ± 294.3). This was significantly
higher than exposure weightings obtained for flat-back turtles nesting in either the Gulf of Carpentariaand Torres Strait (97.69; 1.51 ± 13.58) or eastern Aus-tralia (94.57; 4.73 ± 23). Eastern Australian sitesappeared significantly more light-exposed than Gulfof Carpentaria and Torres Strait sites (Kruskal-Wallisχ2[2, N = 115] = 49.58, p < 0.01).
When using the mean light exposure values, flat-back nesting sites on the North West Shelf appearedto be exposed to significantly more light pollution(23.62; 4.78 ± 9.46) than sites in eastern Australia(5.25; 0.53 ± 1.98) (Mann-Whitney U = 16, n1 = 4, n2 =39, p < 0.01).
Region
For each species with multiple management unitswithin Australia, it was the management units nest-ing in WA that were exposed to the highest levels oflight pollution (Table 4). In particular the DampierArchipelago, Barrow Island, Montebello Islands andCape Range Ningaloo were identified as potentialhigh-risk nesting sites for >1 species (Figs. 2 & 3).
DISCUSSION
Marine turtles spend 100% of their critical breed-ing life-history phase (egg laying, incubation andhatchling emergence) out of the water on beaches.Moreover, turtles migrate from dispersed foraginggrounds to aggregate at these breeding sites (e.g.Limpus et al. 1992). Thus, effective, long-term con-servation strategies require the protection of thesedevelopmental habitats (Troëng & Rankin 2005).Since successful turtle nesting is strongly hinderedby the presence of artificial light (Witherington &Martin 2000) and the effective management of lightpollution adjacent to turtle nesting sites may be bothexpensive and time-intensive (e.g. Fuentes et al.2009), the identification of nesting sites at greatestrisk from light pollution is crucial to ensure that lim-ited conservation resources are allocated most effec-tively (e.g. Fuentes et al. 2011).
We used satellite imaging as a broad-scale tool forthe identification and comparison of nesting locationspotentially vulnerable to coastal light pollution atecologically relevant scales. An important caveat toour study, given the coarse spatial scale of the datasetutilized, is that beachfront lighting in an otherwiseundeveloped area may not register in the satellitedata, but would retain the potential to disrupt turtle
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Population Mean light Max. lightmanagement exposure exposureunits
1 North West Shelf Western Australian flatback turtles hawksbill turtles2 Western Australian North West Shelf hawksbill turtles flatback turtles3 North West Shelf North West Shelf green turtles green turtles
Table 4. The 3 marine turtle management units in Australiapotentially most exposed to light pollution, using the mean(mean light exposure) and maximum (maximum light
exposure) pixel values
Kamrowski et al.: Light pollution and marine turtles
nesting (Witherington & Martin 2000). However,lights from very small residential settlements (pop -ulations of <300 people) in remote regions of Australia — including islands of the Torres Straitwhere no industry or commercial entities exist —were picked up by the satellite data. Therefore, it isunlikely that significant sources of potentially disori-enting light exist in Australia which were not identi-fied in the present study.
Furthermore, an examination of ourdata in light of evidence regarding thebeach-scale impact of light pollution inAustralia supports the value of ourmethodology. We determined thatnesting sites on the North West Shelfof WA and along the Woongarra coastof QLD were the sites facing the high-est potential risk from light pollutionAustralia-wide, with nest sites innorthern Australia appearing to beminimally exposed to light pollution.In his comprehensive review of marineturtles within Australia, Limpus (2009)evaluated the threat of light pollutionfor each species of turtle, using dataand observations from researchersworking on the ground. Reflecting ourdata, Limpus (2009) found no evidenceof turtles disrupted by artificial light innorthern Australia, but highlightedthe Woongarra coast of QLD and theNorth West Shelf in WA as areaswhere disorientation of hatchlingsregularly occurred due to the presenceof artificial lights. Consequently, themethod we have presented offers auseful means of highlighting particu-lar regions, over a large spatial scale,where marine turtle nesting may be atrisk from light pollution. Our methodalso allows for the magnitude of poten-tial light pollution risk to be comparedacross nest sites. Once potentiallyhigh-risk sites for management unitshave been identified, the next step formanagers should be an on-the-groundassessment to confirm the risk identi-fied by the broad-scale analysis pre-sented here, and to subsequentlydetermine necessary beach-specificmanagement actions.
Overall our findings indicate thatthere is large spatial variation in levels
of coastal light pollution across Australia, whichmight be expected to cause disruption to marine tur-tles. Although the majority of marine turtle nesting inAustralia appears to be minimally affected by lightpollution, large proportions of nesting hawksbill, flat-back, green and loggerhead turtles do appear to beexposed to light pollution, especially in WA andalong the urban coast of Queens land. Moreover, tur-tles at these sites are potentially exposed to light sub-
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Fig. 2. The 10 nesting sites (by species) in Australia potentially at highest riskfrom maximum light exposure (maximum pixel values), with light pollutionexposure values (percent nesting × risk value) in parentheses. Cc: loggerheadCaretta caretta; Cm: green Chelonia mydas; Ei: hawksbill Eretmochelys
imbricata; Nd: flatback Natator depressus
Fig. 3. The 10 nesting sites (by species) in Australia potentially at highest riskfrom mean light exposure (mean pixel values), with light pollution exposurevalues (percent nesting × risk value) in parentheses. Six of the sites occurredin SE QLD, 4 in WA (values inset left, see Fig. 2 for locations). Cc: loggerheadCaretta caretta; Cm: green Chelonia mydas; Ei: hawksbill Eretmochelys
imbricata; Nd: flatback Natator depressus
Endang Species Res 19: 85–98, 2012
stantially brighter than natural night-time brightness,with most affected nesting sites potentially exposedto light pollution of Category 5 or higher (>1 to 3times brighter than natural night-time brightness).This is important be cause ecological and behaviouralstudies have found that hatchling disorientation canbe caused by very low levels of artificial light (With-erington & Martin 2000). The pervasive levels of lightpollution we found would be expected to disrupt tur-tle orientation at these sites.
Certain management units appear to face extremepotential risk, with 99.8% of hawksbill turtle nestingsites and 87.4% of flatback turtle nesting sites in WAdetermined to be at risk from light pollution. This issubstantially higher than previous estimates of 12and 42% for hawksbill and flatback turtles, respec-tively, in the region of the Barrow, Lowendal andMontebello Islands of WA (Pendoley 2005). However,where we calculated exposure within an area 25 kmin radius from the nesting site, Pendoley (2005) con-sidered the effect of lights within a radius of 1.5 km —a conservative radius considering the distance overwhich lights have been known to disrupt turtle be -haviour on land (Hodge et al. 2007) and may poten-tially affect hatchling behaviour in the sea. Turtlehatchlings swim slowly, covering only 1.5 km h−1 orless (Frick 1976, Salmon & Wyneken 1987). However,swimming hatchlings show oriented swimming be -haviour for longer than 24 h (Salmon & Wyneken1987) and, in the absence of wave cues to guide themoffshore, have been found to be more susceptible todisorientation from onshore light cues (Lorne &Salmon 2007). Consequently, in the absence of wavecues, artificial lights may influence the orientation ofswimming hatchlings over distances >1.5 km. Thehigh proportion of hawksbill and flatback turtle nest-ing sites in WA identified as being at potential risk inthe present study highlights the need for manage-ment and policy approaches that consider synergisticand cumulative impact.
We found that within Australia a few nesting sitesin WA, which support nesting by multiple species,appear to be the sites most vulnerable to light pollu-tion—namely the Dampier Archipelago, the Monte-bello Islands, Varanus Island and Barrow Island. Thepresence of light pollution at these sites is wellknown. This is one of WA’s, and indeed Australia’s,most productive regions for resource extraction, pro-cessing and shipping, with 59% of WA’s oil and 93%of WA’s gas being produced on the North West Shelf(Department of Environment and Conservation 2007).The influence of lights and flares from hydrocarbonindustrial plants has been categorised as a current
major pressure on turtles in this region (Pendoley2000, Environment Australia 2003, Department ofEnvironment and Conservation 2007, EnvironmentalProtection Agency 2010), and State Government leg-islation, plus industry-specific management plans,are in place to regulate the of use of appropriatelighting by existing and future industry (Departmentof Environment and Conservation 2007, 2008, Chev -ron Australia 2009, Environmental Protection Agency2010, BHP Billiton 2011).
Despite acknowledgement of the existence of lightpollution in this region, we demonstrate that nest siteexposure to light pollution may be far higher in WAthan elsewhere in Australia, and, collectively, it couldimpact turtles at ecological scales, since multiple nest-ing sites appear affected within turtle managementunits. This indicates that rigorous light pollution man-agement is vital, particularly given the im portance ofthe turtle management units which nest here. The WAmanagement units of hawksbill, green, loggerheadand flatback turtles are globally significant for theirrespective species (Seminoff 2002, Mortimer & Don-nelly 2008). Moreover, our results are conservativedue to our use of light data from 2006; since that timedevelopment of the region has continued.
In 1 recent liquefied natural gas (LNG) develop-ment, the proponents were legally obliged underministerial conditions attached to their AustralianGovernment approval to develop management plansfor marine turtles and develop and implement miti-gation plans for light pollution. Under these plans,site-specific light pollution is audited annually(Chev ron Australia 2009). Yet, although light pollu-tion in this region seems to be being addressed byindividual producers at site-specific scales (e.g. Sin-clair Knight Merz 2008, Chevron Australia 2009, BHPBilliton 2011), the cumulative effect of extreme lightlevels over a small geographic region, or as it relatesto specific turtle management units, is not addressedby State or Australian Government legislation or pol-icy (Department of Environment and Conservation2008).
We also found that nesting sites in eastern Aus-tralia appeared to be at high risk from light pollution,particularly in the case of loggerhead turtles alongthe Woongarra coast of south-east QLD. Interestinglythese nesting locations were only identified as beingat high potential risk when using the mean lightexposure values, i.e. the mean pixel value within the25 km buffer. This suggests that light pollution ineastern Australia may be characterised by areas ofwidespread, moderate levels of light pollution fromdispersed urban settlements, as opposed to small
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areas of high levels of localised light pollution fromintense industrial development on an otherwise rela-tively unsettled coastline in WA. This has implica-tions for management in that it may be more econom-ically and logistically feasible to implement lightmitigation in WA, by targeting small areas of highlight pollution produced by a limited number of con-tributors, rather than targeting a larger area produc-ing moderate levels of light pollution, with multiplecontributors (e.g. Fuentes et al. 2011).
Industrial development is increasing along theeastern Australian coast, as well as in other turtlenesting locations worldwide, including Qatar (Tayab& Quiton 2003) and India (Fernandes 2008). Giventhe findings from the present study, which suggestthat the amount of light pollution produced by similarexisting industrial developments in WA may pose avery high risk to nesting marine turtles, the adequatemanagement of light generated by proposed and on-going industrial developments should be consideredextremely important by managers and policy mak-ers. One of the challenges currently faced by indus-try, regulators and researchers involved with turtleconservation is the lack of monitoring tools to exam-ine low, ecologically relevant, light levels, or tools totest the effects of skyglow.
By virtue of the collection method, only night-timelight levels on cloud-free nights are represented bythe satellite data (Elvidge et al. 2001). A recent studyhas demonstrated that cloud cover substantiallyincreases skyglow, since unused light escaping up -wards into the atmosphere is reflected back down toEarth by clouds (Kyba et al. 2011). The authors arguethat investigations into the ecological effects of lightpollution need to take cloud coverage into considera-tion. Thus, light pollution levels and the subsequentimpacts of this light at turtle nesting sites on cloudynights may be even higher than suggested by ourfindings.
CONCLUSIONS
Light pollution is an indisputable problem for marine turtles, and, given existing and continuingcoastal development along many of the world’s turtlenesting beaches, it is also likely to be a pervasiveissue. Studies investigating the impacts of light pollu-tion on marine turtles are numerous; yet, since mostof this research is beach or region specific, under-standing the risks posed to breeding marine turtles ata management unit scale, from light generated bydifferent producers, has not been possible.
Our study is the first of its kind. The methodologywe present provides a useful first step for effectivelymanaging the disruptive influence of light pollutionon marine turtles, at an ecologically relevant scale.The large spatial scale we used emphasises the sig-nificant risk that concentrated light produced byindustrial developments, and diffuse light generatedby urban complexes, may pose to nesting marine tur-tles. We also highlight the regions of Australia whereturtle nesting appears to be at highest risk from lightpollution, namely southerly nesting sites on both thewest and east coasts, with sites in northern Australialeast affected.
In view of the multitude of threats faced by turtles,consideration of this information is extremely rele-vant for managers, especially in regions of plannedindustrial development. This is particularly impor-tant in regions with multiple contributors to artificiallight production, since cumulative light levels maynot be addressed in management plans. Further-more, the identification of concentrated and diffuselight pollution indicates that management strategiesmay need to be tailored depending on the sourcesgenerating the artificial light.
We recommend that light-mitigation strategies beimplemented as standard as development increasesalong the Australian coastline, and we urge man-agers of marine turtles elsewhere to recognise thehuge potential for disruption that light generated byindustrial and urban developments may cause.
Acknowledgements. We thank SEQ Catchments andQDERM for providing the nesting data, P. Whittock for infor-mation on nest sites in WA, P. Ridd for advice regarding theconversion of light from radiance to luminance values andD. Ziskin at NOAA for assistance with the night-light datafiles. We also thank J. Hazel for useful comments, and A.Edwards for help with the figures. Advice from 2 anonymousreviewers greatly improved the manuscript. We acknowl-edge the image and data processing of night-light data byNOAA’s National Geophysical Data Center and the DMSPdata collected by the US Air Force Weather Agency. Thismanuscript forms part of R.L.K.’s PhD research at JamesCook University. R.L.K. is supported by the Northcote TrustGraduate Scholarship Scheme.
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Editorial responsibility: Brendan Godley, University of Exeter, Cornwall Campus, UK
Submitted: June 14, 2012; Accepted: September 4, 2012Proofs received from author(s): November 21, 2012
